Alexei Baevski
Alexei Baevski
Facebook AI Research
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wav2vec 2.0: A framework for self-supervised learning of speech representations
A Baevski, H Zhou, A Mohamed, M Auli
arXiv preprint arXiv:2006.11477, 2020
fairseq: A fast, extensible toolkit for sequence modeling
M Ott, S Edunov, A Baevski, A Fan, S Gross, N Ng, D Grangier, M Auli
arXiv preprint arXiv:1904.01038, 2019
wav2vec: Unsupervised pre-training for speech recognition
S Schneider, A Baevski, R Collobert, M Auli
arXiv preprint arXiv:1904.05862, 2019
Data2vec: A general framework for self-supervised learning in speech, vision and language
A Baevski, WN Hsu, Q Xu, A Babu, J Gu, M Auli
International Conference on Machine Learning, 1298-1312, 2022
Unsupervised cross-lingual representation learning for speech recognition
A Conneau, A Baevski, R Collobert, A Mohamed, M Auli
arXiv preprint arXiv:2006.13979, 2020
vq-wav2vec: Self-supervised learning of discrete speech representations
A Baevski, S Schneider, M Auli
arXiv preprint arXiv:1910.05453, 2019
Pay less attention with lightweight and dynamic convolutions
F Wu, A Fan, A Baevski, YN Dauphin, M Auli
arXiv preprint arXiv:1901.10430, 2019
XLS-R: Self-supervised cross-lingual speech representation learning at scale
A Babu, C Wang, A Tjandra, K Lakhotia, Q Xu, N Goyal, K Singh, ...
arXiv preprint arXiv:2111.09296, 2021
Adaptive input representations for neural language modeling
A Baevski, M Auli
arXiv preprint arXiv:1809.10853, 2018
Facebook FAIR's WMT19 news translation task submission
N Ng, K Yee, A Baevski, M Ott, M Auli, S Edunov
arXiv preprint arXiv:1907.06616, 2019
Unsupervised speech recognition
A Baevski, WN Hsu, A Conneau, M Auli
Advances in Neural Information Processing Systems 34, 27826-27839, 2021
Cloze-driven pretraining of self-attention networks
A Baevski, S Edunov, Y Liu, L Zettlemoyer, M Auli
arXiv preprint arXiv:1903.07785, 2019
On generative spoken language modeling from raw audio
K Lakhotia, E Kharitonov, WN Hsu, Y Adi, A Polyak, B Bolte, TA Nguyen, ...
Transactions of the Association for Computational Linguistics 9, 1336-1354, 2021
Effectiveness of self-supervised pre-training for speech recognition
A Baevski, M Auli, A Mohamed
arXiv preprint arXiv:1911.03912, 2019
Robust wav2vec 2.0: Analyzing domain shift in self-supervised pre-training
WN Hsu, A Sriram, A Baevski, T Likhomanenko, Q Xu, V Pratap, J Kahn, ...
arXiv preprint arXiv:2104.01027, 2021
Masked autoencoders that listen
PY Huang, H Xu, J Li, A Baevski, M Auli, W Galuba, F Metze, ...
Advances in Neural Information Processing Systems 35, 28708-28720, 2022
Self-training and pre-training are complementary for speech recognition
Q Xu, A Baevski, T Likhomanenko, P Tomasello, A Conneau, R Collobert, ...
ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021
Pre-trained language model representations for language generation
S Edunov, A Baevski, M Auli
arXiv preprint arXiv:1903.09722, 2019
Multilingual speech translation with efficient finetuning of pretrained models
X Li, C Wang, Y Tang, C Tran, Y Tang, J Pino, A Baevski, A Conneau, ...
arXiv preprint arXiv:2010.12829, 2020
Scaling speech technology to 1,000+ languages
V Pratap, A Tjandra, B Shi, P Tomasello, A Babu, S Kundu, A Elkahky, ...
Journal of Machine Learning Research 25 (97), 1-52, 2024
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